Answer Sentence Selection (AS2) is the task of identifying sentences that contain the answer to a given question in documents relevant to the question, e.g., retrieved by a search engine. Neural models have highly improved the accuracy on such task and recent approaches, e.g., using Transformer architectures as shown in [1]}, have shown an impressive accuracy.
Although AS2 models seem hard to improve, we note that most previous work does not exploit contextual information in addition to the candidate sentence. For example, [2]} used context in a hierarchical gated recurrent networks but their accuracy is 10-12 points below the state of the art [1]} (as measured on the same exact dataset). Thus we cannot infer that context is really useful in state-of-the-art models for AS2.
However, we do know that some sentences contain ambiguities that cannot be solved without using references to the context outside of the target sentence.
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